English Grammar Discrimination Training Network Model and Search Filtering

Juan Zhao,Wei Wang

doi:10.1155/2021/5528682

Abstract

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory.

Highlights

English grammar is the law of language, reflecting the structure and organization of language
Design of the English Grammar Discriminant Training Network Model. e structural types of English grammar discrimination training reflect the general law of syntactic combination among the components of English grammar discrimination training. e English grammar structure template designed in this paper is a series of related conversion rules composed of the English grammar structure differentiation training and differentiation
Taking the structure types of English grammar to distinguish training templates as the idea, the comprehensive analysis combined with the bridge of dependency syntax analysis may result in the interdependence analysis of both structure and semantics among the components of clauses, but how to realize the computer input sentences according to the matching conditions for English grammar to distinguish the training structure type template rule automatic conversion becomes very important

Summary

Introduction

English grammar is the law of language, reflecting the structure and organization of language. English grammar is the language knowledge and the ability to accurately use the grammatical structure and a skill requiring production training, involving form, meaning, and use. In the appropriate learning stage [1], providing learners with grammar knowledge in an appropriate form is conducive to improving their language learning effect. Neglecting grammar teaching will affect the improvement of learners’ language ability. E search filtering algorithm based on the Network model of English grammar discrimination training scores the nearest user or item prediction, so the efficiency of online recommendation is affected by the number of users or items. Complexity analysis, as well as related schemes and work, a whole set of function structure from representation to weight is constructed and verified by experiments

Related Work

Network Pattern Design of English Grammar Discriminative Training

Example Verification

Conclusions